Interpretive Summary: Time and temperature data collected during the processing, distribution and storage of food are used in computer models to predict consumer exposure to pathogens in food. However, most current models over-predict exposure because they are based on data from sterile food. It has been known for sometime that other microbes in food suppress the growth of pathogens. However, models have not been developed with non-sterile food because it is difficult to follow the growth of pathogens in the presence of other microbes. In the current research, a strain of Salmonella that is found in nature and has a characteristic that allows its growth to be followed in the presence of other microbes was used to develop a computer model that provides better predictions of consumer exposure to Salmonella in ground chicken. Of note, the model predicts less growth of Salmonella on ground chicken than previous models, which were based on data from sterile chicken.

Technical Abstract:
Growth of Salmonella Typhimurium DT104 (ATCC 700408) from a low initial density (0.6 log/g) on non-sterile ground chicken was investigated and modeled as a function of time and temperature (10 to 40C). Data from five replicate challenge studies per temperature were combined and fit to a primary model for determination of maximum specific growth rate (umax), maximum population density (Nmax) and the 95% prediction interval (PI). Non-linear regression was used to obtain secondary models for max, Nmax, PI and the maximum sampling time (tmax), as a function of temperature. Secondary models were combined with the primary model in a computer spreadsheet to create a tertiary model for predicting the variation (95% PI) of pathogen growth among batches of ground chicken. The 95% PI changed in a non-linear manner as a function of temperature from 1.4 to 2.4 to 1.9 log/g at 10, 14 and 40C, respectively. For data used in model development, 93% of observed values for pathogen density at time t, N(t), were in the 95% PI of the tertiary model. This value was not different (P > 0.05) from 95%. For data not used in model development, 94% of observed N(t) were in the 95% PI of the tertiary model. This value was not different (P > 0.05) from 95%. Thus, the tertiary model was successfully validated for predicting the growth of S. Typhimurium DT104 from a low initial density on ground chicken.